Carsten Kern

libalf: the automata learning framework

This paper presents libalf, a comprehensive, open-source library for learning formal languages libalf covers various well-known learning techniques for finite automata (e.g Angluins L*, Biermann, RPNI etc.) as well as novel learning algorithms (such as for NFA and visibly one-counter automata) libalf is flexible and allows facilely interchanging learning algorithms and combining domain-specific features in a plug-and-play fashion Its modular design and C++ implementation make it a suitable platform for adding and engineering further learning algorithms for new target models (e.g., Buchi automata).

Replaying play in and play out: synthesis of design models from scenarios by learning

This paper is concerned with bridging the gap between requirements, provided as a set of scenarios, and conforming design models. The novel aspect of our approach is to exploit learning for the synthesis of design models. In particular, we present a procedure that infers a message-passing automaton (MPA) from a given set of positive and negative scenarios of the systems behavior provided as message sequence charts (MSCs). The paper investigates which classes of regular MSC languages and corresponding MPA can (not) be learned, and presents a dedicated tool based on the learning library LearnLib that supports our approach.

Learning Communicating Automata from MSCs

This paper is concerned with bridging the gap between requirements and distributed systems. Requirements are defined as basic message sequence charts (MSCs) specifying positive and negative scenarios. Communicating finite-state machines (CFMs), i.e., finite automata that communicate via FIFO buffers, act as system realizations. The key contribution is a generalization of Angluins learning algorithm for synthesizing CFMs from MSCs. This approach is exact-the resulting CFM precisely accepts the set of positive scenarios and rejects all negative ones-and yields fully asynchronous implementations. The paper investigates for which classes of MSC languages CFMs can be learned, presents an optimization technique for learning partial orders, and provides substantial empirical evidence indicating the practical feasibility of the approach.

Smyle: A Tool for Synthesizing Distributed Models from Scenarios by Learning

This paper presents Smyle, a tool for synthesizing asynchronous and distributed implementation models from sets of scenarios that are given as message sequence charts (MSCs). The latter specify desired or unwanted behavior of the system to be. Provided with such positive and negative example scenarios, Smyleemploys dedicated learning techniques and propositional dynamic logic(PDL) over MSCs to generate a system model that conforms with the given examples.

MSCAN: a tool for analyzing MSC specifications

We present the tool MSCan, which supports MSC-based system development. In particular, it automatically checks high-level MSC specifications for implementability.

SMA: the smyle modeling approach

This paper introduces the model-based software development lifecycle model SMA -- the Smyle Modeling Approach -- which is centered around Smyle. Smyle is a dedicated learning procedure to support engineers to interactively obtain design models from requirements, characterized as either being desired (positive) or unwanted (negative) system behavior. Within SMA, the learning approach is complemented by so-called scenario patterns where the engineer can specify clearly desired or unwanted behavior. This way, user interaction is reduced to the interesting scenarios limiting the design effort considerably. In SMA, the learning phase is further complemented by an effective analysis phase that allows for detecting design flaws at an early design stage. Using learning techniques allows us to gradually develop and refine requirements, naturally supporting evolving requirements, and allows for a rather inexpensive redesign in case anomalous system behavior is detected during analysis, testing, or maintenance. This paper describes the approach and reports on first practical experiences.